Project Summary The overall premise of this proposal is to understand how the transformation of the signals from proprioceptive afferents within muscles leads to the representation of movements in somatosensory cortex. Proprioception is a fundamental part of the neural control of movement, evidenced by extreme movement impairments in individuals with proprioceptive loss. However, proprioception has been the topic of far less research compared to motor outputs and other sensory modalities, so how muscle afferent signals arising from muscle spindles and Golgi tendon organs, the peripheral afferents primarily responsible for proprioception, are transformed in the nervous system to represent movements in S1 remains a critical, unsolved mystery. In the proposed work, active and passive movements of the upper limb will be utilized in order to elicit different response characteristics from muscle spindles and Golgi tendon organs, which drive cortical proprioceptive signals in area 3a (S1). Simultaneously, neural activity from area 3a will be recorded using a multi- electrode array, as well as arm segment kinematics, and EMGs of proximal and distal arm muscles. Monkeys will be trained to perform a center-out reaching task while grasping the handle of a two-link planar manipulandum. In the horizontal plane, monkey?s arms will move via unperturbed reaches (ACT condition), passive perturbations applied at-rest (PAS condition), or passive perturbations applied during reaches (COMB condition). These movement conditions will elicit different muscle afferent feedback by altering combinations of movement kinematics, forces, and gamma motor drive. Muscle spindle afferent signals will be modeled using a biophysical model (previously developed by the PI) that accounts for both classical and paradoxical firing characteristics of muscle spindle afferents in active and passive conditions. Golgi tendon organ afferent signals will be modeled using the existing Mileusnic model. How these muscle afferent signals are transformed into movement representations in area 3a will be analyzed in two Specific Aims. In Aim 1, a combination of the modeled afferent signals and data collected from area 3a will be used determine how these signals are represented by area 3a neurons in all three movement conditions. Additionally, network activity recorded from across the array in 3a will be decoded to determine what class of movement variables are encoded in this brain area. In Aim 2, these modeled afferent inputs will be used to drive neural network models of proprioceptive transformations to determine to compare which model produces the most similar neural responses to area 3a. In total, these two aims provide two complimentary approaches for determining the role of proprioceptive afferent signals in cortical proprioceptive representation. This work will provide important foundational knowledge of how cortical proprioception arises from muscle afferents and is important for further understanding the role of proprioception in the neural control of movement.